Probability, Statistics, and Random Processes For Electrical Engineering
This is the standard textbook for courses on probability and statistics, not substantially updated. While helping students to develop their problem-solving skills, the author motivates students with practical applications from various areas of ECE that demonstrate the relevance of probability theory to engineering practice. Included are chapter overviews, summaries, checklists of important terms, annotated references, and a wide selection of fully worked-out real-world examples. In this edition, the Computer Methods sections have been updated and substantially enhanced and new problems have been added.
Why Read This Book
You should read this book if you need a clear, engineering-oriented grounding in probability and random processes that directly supports work in DSP, communications, and spectral analysis. It balances theory, worked engineering examples, and computer-methods exercises so you can both understand the mathematics and apply it to real signal-processing problems.
Who Will Benefit
Undergraduate and graduate ECE students and practicing engineers who need a rigorous but applied introduction to probability and stochastic processes for DSP, communications, or systems analysis.
Level: Intermediate — Prerequisites: Single-variable and multivariable calculus, basic linear algebra, and an introductory signals-and-systems course; some exposure to programming (MATLAB) is helpful but not required.
Key Takeaways
- Apply the axioms of probability and conditional probability to analyze noise and random phenomena in engineering systems
- Compute and interpret moments, characteristic functions, and moment-generating functions for random variables and vectors
- Model and analyze stochastic processes (WSS, ergodicity, autocorrelation) and derive power spectral densities
- Use the Gaussian and Poisson process frameworks to model common noise and arrival processes in communications and radar
- Analyze linear time-invariant systems driven by random inputs and compute output statistics and cross-spectra
- Implement computer-based simulations of random processes and validate analytical results using MATLAB/Octave examples
Topics Covered
- Introduction and Probability Basics
- Random Variables and Common Distributions
- Functions of a Random Variable and Transform Methods
- Joint Distributions and Random Vectors
- Moments, Characteristic Functions, and Limit Theorems
- Conditional Probability, Bayesian Inference, and Parameter Estimation
- Stochastic Processes: Definitions and Classification
- Stationarity, Ergodicity, Autocorrelation and Cross-Correlation
- Power Spectral Density and Spectral Representation
- Gaussian, Poisson, and Markov Processes
- Linear Systems Driven by Random Inputs
- Queueing Models and Applications to Communications
- Computer Methods and Simulation Examples (MATLAB/Octave)
- Selected Applications in Communications, Radar, and Signal Processing
Languages, Platforms & Tools
How It Compares
Covers similar ground to Papoulis & Pillai's Probability, Random Variables, and Stochastic Processes but is generally more engineering-application oriented and includes expanded computer-methods sections.
